Road Signs Research Articles

A two-stage road sign detection and text recognition system based on YOLOv7

We developed a two-stage traffic sign recognition system to enhance safety and prevent tragic traffic incidents involving self-driving cars. In the first stage, YOLOv7 was employed as the detection model for identifying 31 types of traffic signs. Input images were set to 640 × 640 pixels to balance speed and accuracy, with high-definition images split into overlapping sub-images of the same size for training. The YOLOv7 model achieved a training accuracy of 99.2 % and demonstrated robustness across various scenes, earning a testing accuracy of 99 % in both YouTube and self-recorded driving videos. In the second stage, extracted road sign images underwent rectification before processing with OCR tools such as EasyOCR and PaddleOCR. Post-processing steps addressed potential confusion, particularly with city/town names. After extensive testing, the system achieved recognition rates of 97.5 % for alphabets and 99.4 % for Chinese characters. This system significantly enhances the ability of self-driving cars to detect and interpret traffic signs, thereby contributing to safer road travel.

Evaluation of Traffic Sign Occlusion Rate Based on a 3D Point Cloud Space

The effectiveness of road signs is hindered by obstructions, such as vegetation, mutual obstruction of signs, or the road alignment itself. The traditional evaluation of road sign recognition effectiveness is conducted through in-vehicle field surveys. However, this method has several drawbacks, including discontinuous identification, unclear positioning, incomplete coverage, and being time-consuming. Consequently, it is unable to effectively assess the recognition status of road signs at any arbitrary point within the road space. Therefore, this study employed laser scanning to construct a point-surface model, which was based on a point cloud algorithm and SLAM (Simultaneous Localization and Mapping), integrated LiDAR and inertial navigation system data, and optimized the point model after processing steps such as denoising, resampling, and three-dimensional model construction. Furthermore, a method for assessing the highway sign occlusion rate based on the picking algorithm was proposed. The algorithm was applied to an actual road environment, and the occlusion by other items was simulated. The results demonstrated the effectiveness of the method. This new method provides support for the fast and accurate calculation of road sign occlusion rates, which is of great importance for ensuring the safe traveling of vehicles.

Implementation of a Small-Sized Mobile Robot with Road Detection, Sign Recognition, and Obstacle Avoidance

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot’s response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot.

AI Drive Assist: Enhancing Road Safety through Advanced Road Sign Detection and Driver Alerts

AI Drive Assist: Enhancing Road Safety through Advanced Road Sign Detection and Driver Alerts

Re-imagining Cross-Border Relations in the High North: The Case of Kirkenes

Kirkenes is a Norwegian small town located in the High North, bordering Russia. Since the fall of the Soviet Union, the people of Kirkenes have invested heavily in building relations and establishing close ties with their Russian neighbours. Following Russia’s 2022 invasion of Ukraine, the local community must adjust to a new reality. This article explores how conflicting narratives on Kirkenes as a border town inform local discourses on the town’s relationship with Russia, with reference to (1) the issue of Cyrillic Street signs in Kirkenes, (2) twin-city agreements with Russian towns, and (3) Russian access to the port of Kirkenes. Along with a focus on borderland narratives, the article leans on Makarychev & Kuznetsova’s distinction between structural, practical and popular geopolitics to show how cross-border relations at a local level are increasingly shaped by national and international political pressures, strengthening local frustrations and feelings of disconnect with national authorities. Furthermore, the article highlights the importance of history, local conceptions of history, and the weaponization of history in shaping cross-border relations in the Norwegian–Russian borderland.

Prevalence of congenital color vision deficiency (CCVD) among drivers in United Arab Emirates (UAE)

Purpose: This study was conducted to determine the prevalence of color vision deficiency (CVD) and create awareness about the burden of CVD among drivers. Methodology: This was a retrospective cross-sectional study of 3486 participants (men, n = 2789) who applied for/renewed their drivers’ licenses in Al Ain City, Abu Dhabi, United Arab Emirates (UAE). All drivers had their visual acuity (VA) and color vision measured using standard procedures and the data was analyzed using descriptive statistics. Results: The overall prevalence of CVD was 3.13% (95% CI: 2.59–3.75). No participants had visual impairment (best-corrected distance VA worse than 6/18) and no female driver had CVD. The prevalence of CVD among the male drivers was 3.91% (95% CI: 3.25–4.69), and more among the younger drivers (18–29 years). Conclusion: Although the prevalence of CVD was low, to ensure inclusivity and the safety of all road users in the UAE, it is recommended that traffic lights and road signs that mainly use connotative color coding be adapted to include denotative clues which can be interpreted by drivers with CVD. A prospective study in all approved eye testing centers for drivers should be conducted to obtain representative CVD data of drivers in the UAE.

Analysis of the readiness and potential of Nias Island tourist destination

Background: Nias Island has tourism potential that can be enjoyed by visiting tourists. This island is famous for hosting the World Surfing League (WSL). However, Nias Island is struggling with accessibility problems and facilities that are not up to standard, these problems need to be fixed to encourage tourist destinations on Nias Island. Therefore, this study aims to determine the potentials of Nias Island as a tourist destination and to provide solutions for Nias Island, especially in the areas of accessibility and facilities. Methods: This study uses a qualitative method. This study uses the observation method because it is in accordance with the events in the field. Findings: The results of this study are that Nias Island has a lot of tourism potential in the fields of nature, cultural heritage, and natural tourism products. Conclusion: There are several suggestions that can be made to improve the quality of tourist attractions on Nias Island in terms of accessibility (providing road signs) and facilities (improving cleanliness and maximizing existing facilities around tourist attractions). Novelty/Originality of this Study: The study on Nias Island’s tourism potential is novel in its comprehensive approach to addressing both the island's untapped natural and cultural attractions and the critical issues of accessibility and facility deficiencies. By providing targeted solutions to improve road signs, cleanliness, and the overall quality of tourist facilities, this research offers practical steps to transform Nias Island into a competitive and sustainable tourist destination.

Custom Anchorless Object Detection Model for 3D Synthetic Traffic Sign Board Dataset with Depth Estimation and Text Character Extraction

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions.

Transition from machine intelligence to knowledge intelligence: A multi-agent simulation approach to technology transfer

Abstract The traditional machine intelligence system lacks deep understanding and reasoning ability. This study took the automatic driving system in multi-agent as an example to bring higher-level intelligence and decision-making ability to automatic driving through knowledge intelligence. It obtained real-world geographic information data from OpenStreetMap, preprocessed the data, and built a virtual environment. The inception model was used to identify information in environmental images, and the knowledge information of traffic regulations, road signs, and traffic accidents was expressed to build a knowledge map. The knowledge related to automatic driving was integrated, and automatic driving training was carried out through the reward mechanism and the deep Q-network (DQN) model. About 13 kinds of traffic situations were set up in the virtual environment, and the traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agent were compared. The results show that the average number of accidents in 100,000 km of traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agents was 3 and 1.4, and the average number of violations in 100,000 km was 4.3 and 1.8, respectively. The average graphics processing unit (GPU) utilization rate of knowledge fusion autonomous driving in 13 virtual environments was 75.9%, and the average peak GPU utilization rate was 96.1%. Knowledge fusion of autonomous driving multi-agents can effectively improve the safety of autonomous driving and enable autonomous driving multi-agents to have a higher level of decision-making ability.

Bridging cultures and languages through cosmopolitan translation: a case of Macao’s Chinese-Portuguese bilingual street signs

ABSTRACT Macao's bilingual street signs, displaying both Chinese and Portuguese inscriptions, embody a hybrid identity, revealing a dynamic interplay of translation within a cosmopolitan context. This study analyzes diverse translational practices, including awkward, favorable, mismatched, and multiple translations, observed through participant observation. It argues that these translational acts empower the Chinese language, fostering novel aesthetics and attitudes while demonstrating the harmonious coexistence and enduring integration of cross-cultural (re)inventions. The multilingual urban landscape of Macao provides a rich terrain for exploring the complexities and inherent nature of translation.

Lane Line and Object Detection Using Yolo v3

In the contemporary age, creating autonomous vehi- cles is a crucial starting point for the advancement of intelligent transportation systems that rely on sophisticated telecommu- nications network infrastructure, including the upcoming 6g networks. The paper discusses two significant issues, namely, lane detection and obstacle detection (such as road signs, traffic lights, vehicles ahead, etc.) using image processing algorithms. To address issues like low accuracy in traditional image processing methods and slow real-time performance of deep learning-based methods, barriers for smart traffic lane and object detection algorithms are proposed. We initially rectify the distorted image resulting from the camera and then employ a threshold algorithm for the lane detection algorithm. The image is obtained by extracting a specific region of interest and applying an inverse perspective transform to obtain a top-down view. Finally, we apply the sliding window technique to identify pixels that belong to each lane and modify it to fit a quadratic equation. The Yolo algorithm is well-suited for identifying various types of obstacles, making it a valuable tool for solving identification problems. Finally, we utilize real-time videos and a straightforward dataset to conduct simulations for the proposed algorithm. The simula- tion outcomes indicate that the accuracy of the proposed method for lane detection is 97.91% and the processing time is 0.0021 seconds. The proposal for detecting obstacles has an accuracy rate of 81.90% and takes only 0.022 seconds to process. Compared to the conventional image processing technique, the proposed method achieves an average accuracy of 89.90% and execution time of 0.024 seconds, demonstrating a robust capability against noise. The findings demonstrate that the suggested algorithm can be implemented in self-driving car systems, allowing for efficient and fast processing of the advanced network.

Understanding level of Chinese traffic signage among children aged 7–11 years: A pilot study

Road traffic injuries are one of the main causes of death among children. In recent years, the incidence and casualty rates of traffic accidents have increased year by year, which is a major challenge faced by safety organizations and governments in various countries, especially in developing countries. Among them, correct understanding of road traffic signs is a factor in reducing accidents. Therefore, identifying traffic signs has become an important indicator of road safety education. This study aimed to understand the understanding level of existing road traffic signs among Chinese children aged 7–11. 30 children aged 7–11 from Guangzhou participated in this study.The working title “Traffic Signage understanding Test questionnaire” was used as the tool for data collection. The questionnaire measured three aspects of child road users' understanding, including verbal label, action and consequences. Three experts in child psychology and cognition reviewed the questionnaire and validated its face and content. The survey results show that compared with actions and consequences, children have a lower understanding of verbal labels of signs, but overall, most children understand traffic signs incorrectly or do not understand them. This is statistically significant in reducing China's road traffic casualty rate. It is also recommended to design traffic signs that are suitable for children to understand.

Traffic Sign Board Recognition and Voice Alert System using CNN

Street to guarantee a secure and efficient flow of traffic. Street accidents sometimes occur on account of carelessness in reading traffic signs incorrectly. The suggested framework aids in recognizing the stop sign and giving a voice warning to the motorist for the speaker to make their point, and crucial decisions. The proposed framework is prepared Using a Convolutional Neural Network (CNN), which aids with the recognition and arranging of rush hour congestion sign pictures. To increase precision, a number are of classes generated and characterized on a particular dataset. Utilized was the German Traffic Sign Benchmarks Dataset, which includes 51,900 pictures of road signage in 43 classifications. Around 98.52 percent during execution was precise. After the framework recognizes the sign, the driver is informed through a voice alarm issued through the speaker. The suggested framework also includes a section where drivers are warned about nearby traffic signs so they can keep track of which laws to follow while on a highway. The system’s goal is to protect the driver, passengers, and pedestrians from harm.

TRAFFIC SIGN RECOGNITION IN CHALLENGING WEATHER CONDITIONS USING CONVOLUTIONAL NEURAL NETWORKS

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

Investigation on the Impact of Ancillary Roadworks on Road Safety in Uganda: A Case of Kampala –Jinja Road

Increased accidents on Ugandan roads compelled the construction of road ancillaries, with a major aim of improving road safety, to attain sustainable economic development and further achieve SDG target 3.6 and SDG target 9.1. This study investigated the impact of ancillary roadworks on road safety in Uganda, a case of the Kampala – Jinja road. The study used a correlation research design with a quantitative approach. Primary data were collected from 30 field traffic officers, 19 drivers, 03 OC traffic police stations, 36 road engineers, 01 IOV, 01 RTO, and 12 market leaders whereas secondary data were extracted from accident data from Uganda Police. Questionnaire data were analysed with SPSS version 24.0. Results indicated that 39% of installed informatory signs, 47% of installed regulatory signs, and 60% of installed warning signs had been vandalised, exposing road users to the risk of accidents. Guardrails were still existing but some had rusted and others covered with grown grass, making them not seen by road users. The majority of road accidents were serious, accounting for 149 (46.3%) cases, followed by 130 fatal accidents (40.4%) and 43 (13.3%) minor accidents for the period from 2017 to 2022. Correlation results indicated that all ancillary road works were significant except road signs. Yet, regression results indicated that road marking (βeta = 0.307), guardrails (βeta = 0.286), and humps and rumble strips (βeta = 0.360) have a positive significant effect on road safety unlike road lights, road signs, walkways, and service lanes which were insignificant. Police traffic law enforcement and regular improvement on road infrastructure are required. It is concluded that road marking, guardrails, humps, and rumble strips significantly affect road safety. The study recommended that the construction of humps and rumble strips of moderate size, road marking with higher retro reflectivity (>200 mcd/lx/m2), and strong guardrails, can help in ensuring road safety with the support of enforcement of traffic laws of Uganda.

Awareness and Detection of the Gridlock Manifestation for Vehicles Voice Alert using CNN

To ensure a smooth and secure flow of traffic, road signs are essential. A major cause of road accidents is negligence in viewing the Traffic signboards. The proposed system helps in recognizing the traffic sign and sending a voice alert through the speaker to the driver so that he or she may take necessary decisions. The proposed system is trained using a convolutional neural network (CNN) which helps in traffic sign image recognition and classification. Following the detection of the sign by the system, a voice alert is sent through the speaker which notifies the driver and also contains a section where the vehicle driver is alerted about the traffic signs. The aim of this system is to ensure the safety of the vehicle’s driver and passengers. Keywords: convolutional neural network (CNN), deep learning, traffic signs.

The influence of thunderstorm type on extreme near-surface wind speeds: Iowa case study

The derecho of August 10, 2020 that impacted Iowa and neighboring states is the costliest thunderstorm disaster in U.S. history. A derecho, which features prolonged and destructive winds, is a type of thunderstorm which has different near-surface wind generating mechanisms than typically assumed in wind engineering. The derecho event prompted two research questions with respect to wind engineering: (1) should derechos, and more broadly thunderstorm type, be considered separately? and (2) how unique is this particular derecho event?Wind speeds for design in the U.S. are currently estimated using an approach where probability distributions of all thunderstorm winds are analyzed through a mixed distribution. Using Automated Surface Observing System (ASOS) and radar data from the National Weather Service, thunderstorm events with ASOS wind speeds >58 mph (26 m/s) were classified by thunderstorm type: single-cell, multicellular, or supercell thunderstorms in Iowa. An extreme value analysis was done on each thunderstorm type. Multicellular thunderstorms, like the August 2020 derecho, dominate the extreme wind climatology in Iowa. Evaluating the uniqueness of the derecho event required a post-damage assessment. Analysis from failed and unfailed street signs and nearby anemometry was used to estimate peak wind speeds approaching 120 mph (50 m/s).

Implementasi Sistem Navigasi Terintegrasi untuk Pembuatan Petunjuk Jalan di Jalan Juma Gaja, Sikunihan 1, Sikunihan 2, dan Siboras

Road signs are visual or verbal information that provides road users with instructions or directions to help them reach their destination safely and efficiently. Traffic signs can be traffic signs, directional signs, road markings, or verbal instructions given by GPS navigation or road guidance. The purpose of directions is to provide clear information regarding direction, distance and travel route to the desired destination. The aim of this research is to develop effective guidelines for the Juma Gaja, Sikunihan 1, Sikunihan 2 and Siboras areas. By analyzing user needs and integrating GPS technology, we have designed a navigation system that can provide accurate and efficient directions. Field test methodology is used to ensure system effectiveness. The results show that the system implemented has succeeded in providing guidance that is timely and easy to use. The practical implication of this research is increasing mobility and transportation affordability in the study area..

ROAD TRAFFIC ACCIDENTS FACTOR ON RURAL ARTERIAL ROADS

In Indonesia, traffic accidents occur on 23.55% of national roads and 75.08% of rural roads. The rural roads were identified as accident-prone areas. As a result, unsafe roads hinder population mobility, disrupt daily life, and reduce access to education, employment, and essential services, thereby affecting the well-being and development of rural communities. To identify the dominant factors that influence the frequency of collisions, a more thorough analysis of the multifactorial causes of traffic accidents in accident-prone areas is necessary. This study focused on analyzing the factors that contribute to road traffic accidents. Cross-tabulation was done using chi-square analysis. The chi-square test does not assume a normal distribution of data, which is beneficial when dealing with real-world data that may not follow normal distribution patterns. This flexibility makes it a robust choice for analyzing traffic accident data, which can be highly variable and not normally distributed. The results of the chi-square analysis for significance values below 0.05 indicate that there is a correlation between accident-causing factors (the independent variable) and collision frequency (the dependent variable). The results indicate that human factors, such as carelessness and high speed, as well as road and environmental factors, such as horizontal alignment, road width, clear zone, road signs, road markings, and land use, influence traffic accidents. Infrastructure factors such as horizontal alignment, road width, clear zones, shoulders, signs, and markings influence traffic accidents because they directly impact road user safety. Non-standard road geometry (horizontal alignment, road width, shoulder width, and clear zone) combined with incomplete road safety facilities (signs and markings) have the potential to cause traffic accidents. Therefore, harmonizing road geometry and equipment is necessary to improve arterial road safety, especially in accident-prone areas. Furthermore, speed management across a variety of land uses is required to reduce traffic accidents.

Pembangunan Fasilitas Peningkatan Sumber Daya Manusia di Kelurahan Makbon

Makbon sub district is a sub district located in Makbon District, Sorong District, Southwest Papua Province. The Topographical conditions of the villages in Makbon District are mostly located in coastal areas with a total of 15 villages/sub districts. Public facilities are facilities provided for the public interest in the sense of facilities that are used for the benefit of all society in carrying out and making it easier for society to carry out their daily activities. Increasing development in villages is really needed by the community, especially to stimulate economic activities. One of the contributions of the Saint Paul Sorong Polytechnic in Papua, especially in Sorong, is providing community service. There are several things that the lecturer team can do to help the community, namely making sub district boundary moments, making street signs and making street lights for the community service in Makbon to develop facilities and human resources (HR) for the quality of life of the people in Makbon district, Sorong regency, Southwest Papua Province.